We have been applying big geospatial data processing techniques to vehicle GPS data collected over several months in 2015 for Maryland roads in order to reconstruct spatiotemporal vehicle trajectories and understand how the volume of traffic varies on different types of roads. Volume of traffic or vehicle miles traveled estimates contribute to improved safety, mobility, and carbon emission tracking for vehicles on US roads. Traditional map matching algorithms, for example, algorithms based on Hidden Markov Models that match GPS trajectories onto road networks take a very long time to process millions of GPS trajectories. In this talk, I will discuss strategies for reconstructing vehicle trajectories from GPS trip data including some of the issues that we have encountered working with massive GPS datasets, where depending on the sampling, we could be working with millions of trips and hundreds of millions of waypoints. I’ll share some of our results so far including visualizations of the travel activity patterns.